{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T09:08:08Z","timestamp":1784192888017,"version":"3.55.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Hypertension is a major contributor to cardiovascular disease and premature mortality. Yet despite extensive research, aspects of the disease remain incompletely understood. Indeed, even with the availability of numerous medications, achieving optimal blood pressure control continues to be a challenge for some patients. These challenges may be attributable to the inherent heterogeneity in disease phenotype and to comorbidities. The recent explosion in healthcare data has presented an opportunity for progress: Artificial intelligence (AI), which is particularly adept in identifying patterns and regularity in a dataset, may facilitate a breakthrough in hypertension. This review summarizes the ways in which AI has transformed clinical practices, patient care, medical education, and research methodologies in hypertension. The focus of the review is on ChatGPT, an AI-powered conversational model that has enhanced data analysis, decision support, and patient education and has the potential to revolutionize hypertension research, diagnosis, and treatment.<\/jats:p>","DOI":"10.1093\/ajh\/hpaf045","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:50:31Z","timestamp":1743641431000},"page":"621-627","source":"Crossref","is-referenced-by-count":5,"title":["A Heart-to-Heart With ChatGPT: AI Applications in Hypertension"],"prefix":"10.1093","volume":"38","author":[{"given":"Anita T","family":"Layton","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, University of Waterloo , Waterloo, Ontario ,","place":["Canada"]},{"name":"Cheriton School of Computer Science, University of Waterloo , Waterloo, Ontario ,","place":["Canada"]},{"name":"Department of Biology, University of Waterloo , Waterloo, Ontario ,","place":["Canada"]},{"name":"School of 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